# The Biopython testing framework

Biopython has a regression testing framework (the file `run_tests.py`) based on unittest, the standard unit testing framework for Python. Providing comprehensive tests for modules is one of the most important aspects of making sure that the Biopython code is as bug-free as possible before going out. It also tends to be one of the most undervalued aspects of contributing. This chapter is designed to make running the Biopython tests and writing good test code as easy as possible. Ideally, every module that goes into Biopython should have a test (and should also have documentation!). All our developers, and anyone installing Biopython from source, are strongly encouraged to run the unit tests.

## Running the tests

When you download the Biopython source code, or check it out from our source code repository, you should find a subdirectory call `Tests`. This contains the key script `run_tests.py`, lots of individual scripts named `test_XXX.py`, and lots of other subdirectories which contain input files for the test suite.

As part of building and installing Biopython you will typically run the full test suite at the command line from the Biopython source top level directory using the following:

    $ python setup.py test

This is actually equivalent to going to the `Tests` subdirectory and running:

    $ python run_tests.py

You’ll often want to run just some of the tests, and this is done like this:

    $ python run_tests.py test_SeqIO.py test_AlignIO.py

When giving the list of tests, the `.py` extension is optional, so you can also just type:

    $ python run_tests.py test_SeqIO test_AlignIO

To run the docstring tests (see section Writing doctests), you can use

    $ python run_tests.py doctest

You can also skip any tests which have been setup with an explicit online component by adding `--offline`, e.g.

    $ python run_tests.py --offline

By default, `run_tests.py` runs all tests, including the docstring tests.

If an individual test is failing, you can also try running it directly, which may give you more information.

Tests based on Python’s standard `unittest` framework will `import unittest` and then define `unittest.TestCase` classes, each with one or more sub-tests as methods starting with `test_` which check some specific aspect of the code.

### Running the tests using Tox

Like most Python projects, you can also use Tox to run the tests on multiple Python versions, provided they are already installed in your system.

We do not provide the configuration `tox.ini` file in our code base because of difficulties pinning down user-specific settings (e.g. executable names of the Python versions). You may also only be interested in testing Biopython only against a subset of the Python versions that we support.

If you are interested in using Tox, you could start with the example `tox.ini` shown below:

    [tox]
    envlist = pypy,py38,py39

    [testenv]
    changedir = Tests
    commands = {envpython} run_tests.py --offline
    deps =
        numpy
        reportlab

Using the template above, executing `tox` will test your Biopython code against PyPy, Python 3.8 and 3.9. It assumes that those Pythons’ executables are named “python3.8“ for Python 3.8, and so on.

## Writing tests

Let’s say you want to write some tests for a module called `Biospam`. This can be a module you wrote, or an existing module that doesn’t have any tests yet. In the examples below, we assume that `Biospam` is a module that does simple math.

Each Biopython test consists of a script containing the test itself, and optionally a directory with input files used by the test:

1. `test_Biospam.py` – The actual test code for your module.

2. `Biospam` [optional]– A directory where any necessary input files will be located. If you have any output files that should be manually reviewed, output them here (but this is discouraged) to prevent clogging up the main Tests directory. In general, use a temporary file/folder.

Any script with a `test_` prefix in the `Tests` directory will be found and run by `run_tests.py`. Below, we show an example test script `test_Biospam.py`. If you put this script in the Biopython `Tests` directory, then `run_tests.py` will find it and execute the tests contained in it:

    $ python run_tests.py
    test_Ace ... ok
    test_AlignIO ... ok
    test_BioSQL ... ok
    test_BioSQL_SeqIO ... ok
    test_Biospam ... ok
    test_CAPS ... ok
    test_Clustalw ... ok
    ...
    ----------------------------------------------------------------------
    Ran 107 tests in 86.127 seconds

### Writing a test using `unittest`

The `unittest`-framework has been included with Python since version 2.1, and is documented in the Python Library Reference (which I know you are keeping under your pillow, as recommended). There is also online documentation for unittest. If you are familiar with the `unittest` system (or something similar like the nose test framework), you shouldn’t have any trouble. You may find looking at the existing examples within Biopython helpful too.

Here’s a minimal `unittest`-style test script for `Biospam`, which you can copy and paste to get started:

    import unittest
    from Bio import Biospam

    class BiospamTestAddition(unittest.TestCase):
        def test_addition1(self):
            result = Biospam.addition(2, 3)
            self.assertEqual(result, 5)

        def test_addition2(self):
            result = Biospam.addition(9, -1)
            self.assertEqual(result, 8)

    class BiospamTestDivision(unittest.TestCase):
        def test_division1(self):
            result = Biospam.division(3.0, 2.0)
            self.assertAlmostEqual(result, 1.5)

        def test_division2(self):
            result = Biospam.division(10.0, -2.0)
            self.assertAlmostEqual(result, -5.0)

    if __name__ == "__main__":
        runner = unittest.TextTestRunner(verbosity=2)
        unittest.main(testRunner=runner)

In the division tests, we use `assertAlmostEqual` instead of `assertEqual` to avoid tests failing due to roundoff errors; see the `unittest` chapter in the Python documentation for details and for other functionality available in `unittest` (online reference).

These are the key points of `unittest`-based tests:

- Test cases are stored in classes that derive from `unittest.TestCase` and cover one basic aspect of your code

- You can use methods `setUp` and `tearDown` for any repeated code which should be run before and after each test method. For example, the `setUp` method might be used to create an instance of the object you are testing, or open a file handle. The `tearDown` should do any “tidying up”, for example closing the file handle.

- The tests are prefixed with `test_` and each test should cover one specific part of what you are trying to test. You can have as many tests as you want in a class.

- At the end of the test script, you can use

      if __name__ == "__main__":
          runner = unittest.TextTestRunner(verbosity=2)
          unittest.main(testRunner=runner)

  to execute the tests when the script is run by itself (rather than imported from `run_tests.py`). If you run this script, then you’ll see something like the following:

      $ python test_BiospamMyModule.py
      test_addition1 (__main__.TestAddition) ... ok
      test_addition2 (__main__.TestAddition) ... ok
      test_division1 (__main__.TestDivision) ... ok
      test_division2 (__main__.TestDivision) ... ok

      ----------------------------------------------------------------------
      Ran 4 tests in 0.059s

      OK

- To indicate more clearly what each test is doing, you can add docstrings to each test. These are shown when running the tests, which can be useful information if a test is failing.

      import unittest
      from Bio import Biospam

      class BiospamTestAddition(unittest.TestCase):
          def test_addition1(self):
              """An addition test"""
              result = Biospam.addition(2, 3)
              self.assertEqual(result, 5)

          def test_addition2(self):
              """A second addition test"""
              result = Biospam.addition(9, -1)
              self.assertEqual(result, 8)

      class BiospamTestDivision(unittest.TestCase):
          def test_division1(self):
              """Now let's check division"""
              result = Biospam.division(3.0, 2.0)
              self.assertAlmostEqual(result, 1.5)

          def test_division2(self):
              """A second division test"""
              result = Biospam.division(10.0, -2.0)
              self.assertAlmostEqual(result, -5.0)

      if __name__ == "__main__":
          runner = unittest.TextTestRunner(verbosity=2)
          unittest.main(testRunner=runner)

  Running the script will now show you:

      $ python test_BiospamMyModule.py
      An addition test ... ok
      A second addition test ... ok
      Now let's check division ... ok
      A second division test ... ok

      ----------------------------------------------------------------------
      Ran 4 tests in 0.001s

      OK

If your module contains docstring tests (see section Writing doctests), you may want to include those in the tests to be run. You can do so as follows by modifying the code under `if __name__ == "__main__":` to look like this:

    if __name__ == "__main__":
        unittest_suite = unittest.TestLoader().loadTestsFromName("test_Biospam")
        doctest_suite = doctest.DocTestSuite(Biospam)
        suite = unittest.TestSuite((unittest_suite, doctest_suite))
        runner = unittest.TextTestRunner(sys.stdout, verbosity=2)
        runner.run(suite)

This is only relevant if you want to run the docstring tests when you execute `python test_Biospam.py` if it has some complex run-time dependency checking.

In general instead include the docstring tests by adding them to the `run_tests.py` as explained below.

## Writing doctests

Python modules, classes and functions support built-in documentation using docstrings. The doctest framework (included with Python) allows the developer to embed working examples in the docstrings, and have these examples automatically tested.

Currently only part of Biopython includes doctests. The `run_tests.py` script takes care of running the doctests. For this purpose, at the top of the `run_tests.py` script is a manually compiled list of modules to skip, important where optional external dependencies which may not be installed (e.g. the Reportlab and NumPy libraries). So, if you’ve added some doctests to the docstrings in a Biopython module, in order to have them excluded in the Biopython test suite, you must update `run_tests.py` to include your module. Currently, the relevant part of `run_tests.py` looks as follows:

    # Following modules have historic failures. If you fix one of these
    # please remove here!
    EXCLUDE_DOCTEST_MODULES = [
        "Bio.PDB",
        "Bio.PDB.AbstractPropertyMap",
        "Bio.Phylo.Applications._Fasttree",
        "Bio.Phylo._io",
        "Bio.Phylo.TreeConstruction",
        "Bio.Phylo._utils",
    ]

    # Exclude modules with online activity
    # They are not excluded by default, use --offline to exclude them
    ONLINE_DOCTEST_MODULES = ["Bio.Entrez", "Bio.ExPASy", "Bio.TogoWS"]

    # Silently ignore any doctests for modules requiring numpy!
    if numpy is None:
        EXCLUDE_DOCTEST_MODULES.extend(
            [
                "Bio.Affy.CelFile",
                "Bio.Cluster",
                # ...
            ]
        )

Note that we regard doctests primarily as documentation, so you should stick to typical usage. Generally complicated examples dealing with error conditions and the like would be best left to a dedicated unit test.

Note that if you want to write doctests involving file parsing, defining the file location complicates matters. Ideally use relative paths assuming the code will be run from the `Tests` directory, see the `Bio.SeqIO` doctests for an example of this.

To run the docstring tests only, use

    $ python run_tests.py doctest

Note that the doctest system is fragile and care is needed to ensure your output will match on all the different versions of Python that Biopython supports (e.g. differences in floating point numbers).

## Writing doctests in the Tutorial

This Tutorial you are reading has a lot of code snippets, which are often formatted like a doctest. We have our own system in file `test_Tutorial.py` to allow tagging code snippets in the Tutorial source to be run as Python doctests. This works by adding special `.. doctest` comment lines before each Python Console (pycon) block, e.g.

    .. doctest

    .. code:: pycon

       >>> from Bio.Seq import Seq
       >>> s = Seq("ACGT")
       >>> len(s)
       4

Often code examples are not self-contained, but continue from the previous Python block. Here we use the magic comment `.. cont-doctest` as shown here:

    .. cont-doctest

    .. code:: pycon

       >>> s == "ACGT"
       True

The special `.. doctest` comment line can take a working directory (relative to the `Doc/` folder) to use if you have any example data files, e.g. `.. doctest examples` will use the `Doc/examples` folder, while `.. doctest ../Tests/GenBank` will use the `Tests/GenBank` folder.

After the directory argument, you can specify any Python dependencies which must be present in order to run the test by adding `lib:XXX` to indicate `import XXX` must work, e.g. `.. doctest examples lib:numpy`

You can run the Tutorial doctests via:

    $ python test_Tutorial.py

or:

    $ python run_tests.py test_Tutorial.py

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